Deep Learning With Tensorflow 2.0, Keras and Python - Loss or Cost Function | Deep Learning Tutorial 11 (Tensorflow Tutorial, Keras & Python)

Loss or a cost function is an important concept we need to understand if you want to grasp how a neural network trains itself. We will go over various loss functions in this video such as mean absolute error (a.k.a MAE), mean squared error (a.k.a MSE), log loss or binary cross entropy. After going through theory we will implement these loss functions in python. It is important to go through this implementation as it might be useful during your interviews (if you are targeting a role of a data scientist or a machine learning engineer) Code: https://github.com/codebasics/deep-learning-keras-tf-tutorial/blob/master/5_loss/5_loss_or_cost_function.ipynb Exercise: Go at the end of the above notebook to see the exercise Do you want to learn technology from me? Check https://codebasics.io/ for my affordable video courses. 🔖 Hashtags 🔖 #lossfunction #costfunction #costfunctionneuralnetwork #lossfunctionneuralnetwork #costfunctiondeeplearning #lossfunctiondeeplearning Why not MSE for logistic regression: https://towardsdatascience.com/why-not-mse-as-a-loss-function-for-logistic-regression-589816b5e03c Next video: https://www.youtube.com/watch?v=pXGBHV3y8rs&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=12 Previous video: https://www.youtube.com/watch?v=Wibxjrxf5ko&list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO&index=10 Deep learning playlist: https://www.youtube.com/playlist?list=PLeo1K3hjS3uu7CxAacxVndI4bE_o3BDtO Machine learning playlist : https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw   Prerequisites for this series:    1: Python tutorials (first 16 videos): https://www.youtube.com/playlist?list=PLeo1K3hjS3uv5U-Lmlnucd7gqF-3ehIh0     2: Pandas tutorials(first 8 videos): https://www.youtube.com/playlist?list=PLeo1K3hjS3uuASpe-1LjfG5f14Bnozjwy 3: Machine learning playlist (first 16 videos): https://www.youtube.com/playlist?list=PLeo1K3hjS3uvCeTYTeyfe0-rN5r8zn9rw   Website: https://codebasics.io/ Facebook: https://www.facebook.com/codebasicshub Twitter: https://twitter.com/codebasicshub